\section{Querying an existing network}
  \subsection{Question 1}
The risk of meltdown during a day if no observations have been made is $0.0.2578$. If there's icy weather, the risk of meltdown is $0.03472$.

  \subsection{Question 2}
Both warning sensors indicate failure : $P(meltdown | pumpFailureWarning, waterLeakWarning) = 0.14535$. Actual pump failure and water leak : $P(meltdown | pumpFailure, waterLeak) = 0.2$.

  \subsection{Question 3}

Sometimes it's very difficult or impossible to make repeated experiments or observation. So it's become very difficult to get accurate numbers for conditional probabilities for some stochastic variables.

For this example, it may be difficult to know the risk of meltdown give a pump failure or/and a water leak.

  \subsection{Question 4}
The domain for variable Temperature could be :
\begin{itemize}
  \item continuous random variable : $[-273 ; +\infty]$ 
  \item discrete random variable : $\left\{EXTREMELY\_COLD, VERY\_COLD, COLD, NORMAL, HOT, VERY\_HOT, EXTREMELY\_HOT\right\}$
\end{itemize}

For the continuous random variable, $P(waterLeak | Temperature)$ may have high probabilities when near $-273^oC$ and decreasing while temperature increase. 

For the discrete random variable, the probability distribution could be as follow :

\begin{tabular}{|c|c|c|}
  \hline
  Temperature & $P(\lnot waterLeak | Temperature)$
                                  & $P(waterLeak | Temperature)$ \\ \hline
  $EXTREMELY\_COLD$  & 0.30 & 0.70  \\ \hline
  $VERY\_COLD$       & 0.35 & 0.65  \\ \hline
  $COLD$             & 0.40 & 0.60  \\ \hline
  $NORMAL$           & 0.50 & 0.50  \\ \hline
  $HOT$              & 0.80 & 0.20  \\ \hline
  $VERY\_HOT$        & 0.95 & 0.05  \\ \hline
  $EXTREMELY\_HOT$   & 0.99 & 0.01  \\ \hline
\end{tabular}


